{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Papua New Guinea case study\n",
    "\n",
    "- BLM calibration (There is no grid)\n",
    "- Solutions clustering\n",
    "- Gap Analysis\n",
    "- Unmet targtes pipeline\n",
    "- FPF calibration (Targets met)\n",
    "- Difference Map (There is no grid)\n",
    "\n",
    "Date: 20 April 2021  \n",
    "Author: tamara.huete@vizzuality.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run marxan_utils.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. MARXAN folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "MARXAN_FOLDER = '/home/jovyan/work/datasets/raw/demo_Papua_New_Guinea/marxan_Papua_New_Guinea'\n",
    "MARXAN_INPUTDATA = 'input.dat'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. BLM calibration (no grid file, can't calculate area to apply rule of thumb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_file_path = f'{MARXAN_FOLDER}/pulayer/pu_base.shp'\n",
    "BLM_calibration(MARXAN_FOLDER,'input.dat',grid_file_path )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotClumping(MARXAN_FOLDER,'input.dat',grid_file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Execute marxan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#execute marxan\n",
    "userInputFile = readInput(MARXAN_FOLDER, MARXAN_INPUTDATA)\n",
    "userInputFile.VERBOSITY = 3\n",
    "saveInput(MARXAN_FOLDER, MARXAN_INPUTDATA,userInputFile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "execute_marxan(MARXAN_FOLDER) ### Runs with the old algorithm but fails with the new one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SCENARIO OUTPUTS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Solutions clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall best solution is 1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1, 7, 8, 3, 10]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bestsols= clusterSolutions(MARXAN_FOLDER,'input.dat',k=5)\n",
    "bestsols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary info 5 most different solutions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Run_Number</th>\n",
       "      <th>Score</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Planning_Units</th>\n",
       "      <th>Connectivity</th>\n",
       "      <th>Connectivity_Total</th>\n",
       "      <th>Connectivity_In</th>\n",
       "      <th>Connectivity_Edge</th>\n",
       "      <th>Connectivity_Out</th>\n",
       "      <th>Connectivity_In_Fraction</th>\n",
       "      <th>Penalty</th>\n",
       "      <th>Shortfall</th>\n",
       "      <th>Missing_Values</th>\n",
       "      <th>MPM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2.224783e+08</td>\n",
       "      <td>1.296565e+08</td>\n",
       "      <td>5114</td>\n",
       "      <td>22902301.0</td>\n",
       "      <td>664787887.0</td>\n",
       "      <td>55640321.0</td>\n",
       "      <td>22902301.0</td>\n",
       "      <td>586245265.0</td>\n",
       "      <td>0.083696</td>\n",
       "      <td>67488.541940</td>\n",
       "      <td>6342.385499</td>\n",
       "      <td>0</td>\n",
       "      <td>0.997146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2.278626e+08</td>\n",
       "      <td>1.349057e+08</td>\n",
       "      <td>5107</td>\n",
       "      <td>22937386.0</td>\n",
       "      <td>664787887.0</td>\n",
       "      <td>55482389.0</td>\n",
       "      <td>22937386.0</td>\n",
       "      <td>586368112.0</td>\n",
       "      <td>0.083459</td>\n",
       "      <td>60452.963580</td>\n",
       "      <td>133286.909344</td>\n",
       "      <td>0</td>\n",
       "      <td>0.995972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2.286940e+08</td>\n",
       "      <td>1.297862e+08</td>\n",
       "      <td>5071</td>\n",
       "      <td>24404852.0</td>\n",
       "      <td>664787887.0</td>\n",
       "      <td>54341769.0</td>\n",
       "      <td>24404852.0</td>\n",
       "      <td>586041266.0</td>\n",
       "      <td>0.081743</td>\n",
       "      <td>68116.993347</td>\n",
       "      <td>4902.865259</td>\n",
       "      <td>0</td>\n",
       "      <td>0.996330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2.249302e+08</td>\n",
       "      <td>1.313776e+08</td>\n",
       "      <td>5097</td>\n",
       "      <td>23095334.0</td>\n",
       "      <td>664787887.0</td>\n",
       "      <td>55355166.0</td>\n",
       "      <td>23095334.0</td>\n",
       "      <td>586337387.0</td>\n",
       "      <td>0.083267</td>\n",
       "      <td>16510.255353</td>\n",
       "      <td>52284.840733</td>\n",
       "      <td>0</td>\n",
       "      <td>0.999915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>2.285567e+08</td>\n",
       "      <td>1.317815e+08</td>\n",
       "      <td>5110</td>\n",
       "      <td>23893771.0</td>\n",
       "      <td>664787887.0</td>\n",
       "      <td>55148977.0</td>\n",
       "      <td>23893771.0</td>\n",
       "      <td>585745139.0</td>\n",
       "      <td>0.082957</td>\n",
       "      <td>5392.868049</td>\n",
       "      <td>45833.407325</td>\n",
       "      <td>0</td>\n",
       "      <td>0.999675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Run_Number         Score          Cost  Planning_Units  Connectivity  \\\n",
       "0           1  2.224783e+08  1.296565e+08            5114    22902301.0   \n",
       "2           3  2.278626e+08  1.349057e+08            5107    22937386.0   \n",
       "6           7  2.286940e+08  1.297862e+08            5071    24404852.0   \n",
       "7           8  2.249302e+08  1.313776e+08            5097    23095334.0   \n",
       "9          10  2.285567e+08  1.317815e+08            5110    23893771.0   \n",
       "\n",
       "   Connectivity_Total  Connectivity_In  Connectivity_Edge  Connectivity_Out  \\\n",
       "0         664787887.0       55640321.0         22902301.0       586245265.0   \n",
       "2         664787887.0       55482389.0         22937386.0       586368112.0   \n",
       "6         664787887.0       54341769.0         24404852.0       586041266.0   \n",
       "7         664787887.0       55355166.0         23095334.0       586337387.0   \n",
       "9         664787887.0       55148977.0         23893771.0       585745139.0   \n",
       "\n",
       "   Connectivity_In_Fraction       Penalty      Shortfall  Missing_Values  \\\n",
       "0                  0.083696  67488.541940    6342.385499               0   \n",
       "2                  0.083459  60452.963580  133286.909344               0   \n",
       "6                  0.081743  68116.993347    4902.865259               0   \n",
       "7                  0.083267  16510.255353   52284.840733               0   \n",
       "9                  0.082957   5392.868049   45833.407325               0   \n",
       "\n",
       "        MPM  \n",
       "0  0.997146  \n",
       "2  0.995972  \n",
       "6  0.996330  \n",
       "7  0.999915  \n",
       "9  0.999675  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary = validateFile(MARXAN_FOLDER,'input.dat',OutputSum)\n",
    "sum_diff =summary[summary['Run_Number'].isin(bestsols)]\n",
    "sum_diff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Gap Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = gapAnalysis(MARXAN_FOLDER, 'input.dat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, anx = plt.subplots(nrows=1, ncols=2,figsize=(10,10), constrained_layout=True)\n",
    "plotGap(df, 'Pre-Marxan', anx[0], post_marxan=False)\n",
    "plotGap(df, 'Post-Marxan', anx[1], post_marxan=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5) Unmet targets pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'None': 'All targets met'}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unmetDecisionTree(MARXAN_FOLDER, 'input.dat',\n",
    "                      lock_out_limit=50, \n",
    "                      high_cost_quantile= 0.7, \n",
    "                      hcost_limit=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
